Pandas Profiling Report

Dataset statistics

Number of variables	18
Number of observations	244
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	26.8 KiB
Average record size in memory	112.5 B

Variable types

Numeric	12
Categorical	6

Alerts

`year` has constant value "2012"	Constant
`df_index` is highly correlated with `Region`	High correlation
`day` is highly correlated with `DMC` and 1 other fields	High correlation
`Temperature` is highly correlated with `RH` and 6 other fields	High correlation
`RH` is highly correlated with `Temperature` and 4 other fields	High correlation
`Rain` is highly correlated with `FFMC` and 6 other fields	High correlation
`FFMC` is highly correlated with `Temperature` and 8 other fields	High correlation
`DMC` is highly correlated with `day` and 9 other fields	High correlation
`DC` is highly correlated with `Rain` and 6 other fields	High correlation
`ISI` is highly correlated with `Temperature` and 8 other fields	High correlation
`BUI` is highly correlated with `day` and 8 other fields	High correlation
`FWI` is highly correlated with `Temperature` and 8 other fields	High correlation
`Region` is highly correlated with `df_index`	High correlation
`Class` is highly correlated with `Temperature` and 7 other fields	High correlation
`df_index` is highly correlated with `Region`	High correlation
`day` is highly correlated with `DC` and 1 other fields	High correlation
`Temperature` is highly correlated with `RH` and 4 other fields	High correlation
`RH` is highly correlated with `Temperature` and 3 other fields	High correlation
`Rain` is highly correlated with `FFMC`	High correlation
`FFMC` is highly correlated with `Temperature` and 8 other fields	High correlation
`DMC` is highly correlated with `FFMC` and 5 other fields	High correlation
`DC` is highly correlated with `day` and 6 other fields	High correlation
`ISI` is highly correlated with `Temperature` and 7 other fields	High correlation
`BUI` is highly correlated with `day` and 6 other fields	High correlation
`FWI` is highly correlated with `Temperature` and 7 other fields	High correlation
`Region` is highly correlated with `df_index`	High correlation
`Class` is highly correlated with `Temperature` and 6 other fields	High correlation
`df_index` is highly correlated with `Region`	High correlation
`Temperature` is highly correlated with `FFMC`	High correlation
`RH` is highly correlated with `FFMC`	High correlation
`Rain` is highly correlated with `FFMC` and 3 other fields	High correlation
`FFMC` is highly correlated with `Temperature` and 8 other fields	High correlation
`DMC` is highly correlated with `FFMC` and 5 other fields	High correlation
`DC` is highly correlated with `FFMC` and 5 other fields	High correlation
`ISI` is highly correlated with `Rain` and 6 other fields	High correlation
`BUI` is highly correlated with `FFMC` and 5 other fields	High correlation
`FWI` is highly correlated with `Rain` and 6 other fields	High correlation
`Region` is highly correlated with `df_index`	High correlation
`Class` is highly correlated with `Rain` and 6 other fields	High correlation
`month` is highly correlated with `year` and 1 other fields	High correlation
`Region` is highly correlated with `year`	High correlation
`Classes` is highly correlated with `year` and 1 other fields	High correlation
`year` is highly correlated with `month` and 4 other fields	High correlation
`Months` is highly correlated with `month` and 1 other fields	High correlation
`Class` is highly correlated with `Classes` and 1 other fields	High correlation
`df_index` is highly correlated with `month` and 9 other fields	High correlation
`day` is highly correlated with `DMC` and 2 other fields	High correlation
`month` is highly correlated with `df_index` and 6 other fields	High correlation
`Temperature` is highly correlated with `df_index` and 10 other fields	High correlation
`RH` is highly correlated with `df_index` and 7 other fields	High correlation
`Ws` is highly correlated with `Temperature` and 1 other fields	High correlation
`Rain` is highly correlated with `Temperature` and 2 other fields	High correlation
`FFMC` is highly correlated with `Temperature` and 8 other fields	High correlation
`DMC` is highly correlated with `df_index` and 10 other fields	High correlation
`DC` is highly correlated with `df_index` and 7 other fields	High correlation
`ISI` is highly correlated with `df_index` and 9 other fields	High correlation
`BUI` is highly correlated with `df_index` and 10 other fields	High correlation
`FWI` is highly correlated with `df_index` and 9 other fields	High correlation
`Classes` is highly correlated with `month` and 10 other fields	High correlation
`Region` is highly correlated with `df_index` and 1 other fields	High correlation
`Months` is highly correlated with `df_index` and 6 other fields	High correlation
`Class` is highly correlated with `month` and 10 other fields	High correlation
`df_index` is uniformly distributed	Uniform
`Region` is uniformly distributed	Uniform
`df_index` has unique values	Unique
`Rain` has 133 (54.5%) zeros	Zeros
`ISI` has 4 (1.6%) zeros	Zeros
`FWI` has 9 (3.7%) zeros	Zeros

Reproduction

Analysis started	2022-06-03 18:49:08.854903
Analysis finished	2022-06-03 18:49:39.217370
Duration	30.36 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

df_index
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct	244
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	122.5

Minimum	0
Maximum	245
Zeros	1
Zeros (%)	0.4%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	0
5-th percentile	12.15
Q1	60.75
median	122.5
Q3	184.25
95-th percentile	232.85
Maximum	245
Range	245
Interquartile range (IQR)	123.5

Descriptive statistics

Standard deviation	71.45049223
Coefficient of variation (CV)	0.5832693243
Kurtosis	-1.214936047
Mean	122.5
Median Absolute Deviation (MAD)	62
Skewness	0
Sum	29890
Variance	5105.17284
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0	1	0.4%
155	1	0.4%
157	1	0.4%
158	1	0.4%
159	1	0.4%
160	1	0.4%
161	1	0.4%
162	1	0.4%
163	1	0.4%
164	1	0.4%
Other values (234)	234	95.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	1	0.4%
1	1	0.4%
2	1	0.4%
3	1	0.4%
4	1	0.4%
5	1	0.4%
6	1	0.4%
7	1	0.4%
8	1	0.4%
9	1	0.4%

Value	Count	Frequency (%)
245	1	0.4%
244	1	0.4%
243	1	0.4%
242	1	0.4%
241	1	0.4%
240	1	0.4%
239	1	0.4%
238	1	0.4%
237	1	0.4%
236	1	0.4%

day
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	31
Distinct (%)	12.7%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.75409836

Minimum	1
Maximum	31
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.1 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	8
median	16
Q3	23
95-th percentile	29
Maximum	31
Range	30
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	8.825059228
Coefficient of variation (CV)	0.5601754557
Kurtosis	-1.198650172
Mean	15.75409836
Median Absolute Deviation (MAD)	8
Skewness	0.002806412611
Sum	3844
Variance	77.88167038
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
1	8	3.3%
17	8	3.3%
30	8	3.3%
29	8	3.3%
28	8	3.3%
27	8	3.3%
26	8	3.3%
25	8	3.3%
24	8	3.3%
23	8	3.3%
Other values (21)	164	67.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	8	3.3%
2	8	3.3%
3	8	3.3%
4	8	3.3%
5	8	3.3%
6	8	3.3%
7	8	3.3%
8	8	3.3%
9	8	3.3%
10	8	3.3%

Value	Count	Frequency (%)
31	4	1.6%
30	8	3.3%
29	8	3.3%
28	8	3.3%
27	8	3.3%
26	8	3.3%
25	8	3.3%
24	8	3.3%
23	8	3.3%
22	8	3.3%

month
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

7	62
8	62
6	60
9	60

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	6
2nd row	6
3rd row	6
4th row	6
5th row	6

Common Values

Value	Count	Frequency (%)
7	62	25.4%
8	62	25.4%
6	60	24.6%
9	60	24.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
7	62	25.4%
8	62	25.4%
6	60	24.6%
9	60	24.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

year
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct	1
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

2012	244

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2012
2nd row	2012
3rd row	2012
4th row	2012
5th row	2012

Common Values

Value	Count	Frequency (%)
2012	244	100.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2012	244	100.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Temperature
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	19
Distinct (%)	7.8%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	32.17213115

Minimum	22
Maximum	42
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.1 KiB

Quantile statistics

Minimum	22
5-th percentile	26
Q1	30
median	32
Q3	35
95-th percentile	37.85
Maximum	42
Range	20
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.63384326
Coefficient of variation (CV)	0.1129500325
Kurtosis	-0.1543103757
Mean	32.17213115
Median Absolute Deviation (MAD)	3
Skewness	-0.1963088795
Sum	7850
Variance	13.20481684
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=19)

Value	Count	Frequency (%)
35	29	11.9%
31	25	10.2%
34	24	9.8%
33	23	9.4%
30	22	9.0%
36	21	8.6%
32	21	8.6%
29	18	7.4%
28	15	6.1%
37	9	3.7%
Other values (9)	37	15.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
22	2	0.8%
24	3	1.2%
25	6	2.5%
26	5	2.0%
27	8	3.3%
28	15	6.1%
29	18	7.4%
30	22	9.0%
31	25	10.2%
32	21	8.6%

Value	Count	Frequency (%)
42	1	0.4%
40	3	1.2%
39	6	2.5%
38	3	1.2%
37	9	3.7%
36	21	8.6%
35	29	11.9%
34	24	9.8%
33	23	9.4%
32	21	8.6%

RH
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	62
Distinct (%)	25.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	61.93852459

Minimum	21
Maximum	90
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.1 KiB

Quantile statistics

Minimum	21
5-th percentile	37
Q1	52
median	63
Q3	73.25
95-th percentile	86
Maximum	90
Range	69
Interquartile range (IQR)	21.25

Descriptive statistics

Standard deviation	14.88420018
Coefficient of variation (CV)	0.2403060176
Kurtosis	-0.5303278714
Mean	61.93852459
Median Absolute Deviation (MAD)	11
Skewness	-0.2379643933
Sum	15113
Variance	221.5394151
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
64	10	4.1%
55	10	4.1%
54	8	3.3%
78	8	3.3%
58	8	3.3%
80	7	2.9%
73	7	2.9%
68	7	2.9%
66	7	2.9%
65	7	2.9%
Other values (52)	165	67.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
21	1	0.4%
24	1	0.4%
26	1	0.4%
29	1	0.4%
31	1	0.4%
33	2	0.8%
34	3	1.2%
35	1	0.4%
36	1	0.4%
37	4	1.6%

Value	Count	Frequency (%)
90	1	0.4%
89	3	1.2%
88	3	1.2%
87	4	1.6%
86	3	1.2%
84	2	0.8%
83	1	0.4%
82	3	1.2%
81	6	2.5%
80	7	2.9%

Ws
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	18
Distinct (%)	7.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.50409836

Minimum	6
Maximum	29
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	1.1 KiB

Quantile statistics

Minimum	6
5-th percentile	11
Q1	14
median	15
Q3	17
95-th percentile	20
Maximum	29
Range	23
Interquartile range (IQR)	3

Descriptive statistics

Standard deviation	2.810178371
Coefficient of variation (CV)	0.181253905
Kurtosis	2.602155825
Mean	15.50409836
Median Absolute Deviation (MAD)	2
Skewness	0.5458812499
Sum	3783
Variance	7.897102476
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=18)

Value	Count	Frequency (%)
14	43	17.6%
15	40	16.4%
13	30	12.3%
17	28	11.5%
16	27	11.1%
18	26	10.7%
19	15	6.1%
21	8	3.3%
11	7	2.9%
12	7	2.9%
Other values (8)	13	5.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6	1	0.4%
8	1	0.4%
9	2	0.8%
10	3	1.2%
11	7	2.9%
12	7	2.9%
13	30	12.3%
14	43	17.6%
15	40	16.4%
16	27	11.1%

Value	Count	Frequency (%)
29	1	0.4%
26	1	0.4%
22	2	0.8%
21	8	3.3%
20	2	0.8%
19	15	6.1%
18	26	10.7%
17	28	11.5%
16	27	11.1%
15	40	16.4%

Rain
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	39
Distinct (%)	16.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.7606557377

Minimum	0
Maximum	16.8
Zeros	133
Zeros (%)	54.5%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0.5
95-th percentile	4.355
Maximum	16.8
Range	16.8
Interquartile range (IQR)	0.5

Descriptive statistics

Standard deviation	1.999405565
Coefficient of variation (CV)	2.628528868
Kurtosis	25.94212272
Mean	0.7606557377
Median Absolute Deviation (MAD)	0
Skewness	4.579070596
Sum	185.6
Variance	3.997622614
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=39)

Value	Count	Frequency (%)
0	133	54.5%
0.1	18	7.4%
0.2	12	4.9%
0.3	10	4.1%
0.4	8	3.3%
0.6	6	2.5%
0.7	6	2.5%
0.5	5	2.0%
1.1	3	1.2%
1.2	3	1.2%
Other values (29)	40	16.4%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	133	54.5%
0.1	18	7.4%
0.2	12	4.9%
0.3	10	4.1%
0.4	8	3.3%
0.5	5	2.0%
0.6	6	2.5%
0.7	6	2.5%
0.8	2	0.8%
0.9	1	0.4%

Value	Count	Frequency (%)
16.8	1	0.4%
13.1	1	0.4%
10.1	1	0.4%
8.7	1	0.4%
8.3	1	0.4%
7.2	1	0.4%
6.5	1	0.4%
6	1	0.4%
5.8	1	0.4%
4.7	1	0.4%

FFMC
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	173
Distinct (%)	70.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	77.88770492

Minimum	28.6
Maximum	96
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	28.6
5-th percentile	47.145
Q1	72.075
median	83.5
Q3	88.3
95-th percentile	92.185
Maximum	96
Range	67.4
Interquartile range (IQR)	16.225

Descriptive statistics

Standard deviation	14.33757088
Coefficient of variation (CV)	0.1840800277
Kurtosis	1.05520829
Mean	77.88770492
Median Absolute Deviation (MAD)	5.7
Skewness	-1.325633262
Sum	19004.6
Variance	205.5659387
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
88.9	8	3.3%
89.4	5	2.0%
89.3	4	1.6%
89.1	4	1.6%
85.4	4	1.6%
79.9	3	1.2%
87	3	1.2%
78.3	3	1.2%
88.1	3	1.2%
47.4	3	1.2%
Other values (163)	204	83.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
28.6	1	0.4%
30.5	1	0.4%
36.1	1	0.4%
37.3	1	0.4%
37.9	1	0.4%
40.9	1	0.4%
41.1	1	0.4%
42.6	1	0.4%
44.9	1	0.4%
45	1	0.4%

Value	Count	Frequency (%)
96	1	0.4%
94.3	1	0.4%
94.2	1	0.4%
93.9	2	0.8%
93.8	1	0.4%
93.7	1	0.4%
93.3	1	0.4%
93	1	0.4%
92.5	2	0.8%
92.2	2	0.8%

DMC
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	166
Distinct (%)	68.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	14.67336066

Minimum	0.7
Maximum	65.9
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	0.7
5-th percentile	1.9
Q1	5.8
median	11.3
Q3	20.75
95-th percentile	41.01
Maximum	65.9
Range	65.2
Interquartile range (IQR)	14.95

Descriptive statistics

Standard deviation	12.36803873
Coefficient of variation (CV)	0.8428906658
Kurtosis	2.487598085
Mean	14.67336066
Median Absolute Deviation (MAD)	6.9
Skewness	1.527652386
Sum	3580.3
Variance	152.9683821
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
7.9	5	2.0%
12.5	4	1.6%
1.9	4	1.6%
9.7	3	1.2%
4.4	3	1.2%
2.6	3	1.2%
8.3	3	1.2%
2.5	3	1.2%
1.3	3	1.2%
16	3	1.2%
Other values (156)	210	86.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0.7	1	0.4%
0.9	2	0.8%
1.1	2	0.8%
1.2	1	0.4%
1.3	3	1.2%
1.7	1	0.4%
1.9	4	1.6%
2.1	1	0.4%
2.2	2	0.8%
2.4	1	0.4%

Value	Count	Frequency (%)
65.9	1	0.4%
61.3	1	0.4%
56.3	1	0.4%
54.2	1	0.4%
51.3	1	0.4%
50.2	1	0.4%
47	1	0.4%
46.6	1	0.4%
46.1	1	0.4%
45.6	1	0.4%

DC
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	198
Distinct (%)	81.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	49.28811475

Minimum	6.9
Maximum	220.4
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	6.9
5-th percentile	7.6
Q1	13.275
median	33.1
Q3	68.15
95-th percentile	158.86
Maximum	220.4
Range	213.5
Interquartile range (IQR)	54.875

Descriptive statistics

Standard deviation	47.61966238
Coefficient of variation (CV)	0.9661489918
Kurtosis	1.614097336
Mean	49.28811475
Median Absolute Deviation (MAD)	23.9
Skewness	1.479041666
Sum	12026.3
Variance	2267.632245
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
8	5	2.0%
7.5	4	1.6%
8.4	4	1.6%
8.3	4	1.6%
7.8	4	1.6%
8.2	4	1.6%
7.6	4	1.6%
17	3	1.2%
45.1	2	0.8%
8.5	2	0.8%
Other values (188)	208	85.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
6.9	1	0.4%
7	2	0.8%
7.1	1	0.4%
7.3	2	0.8%
7.4	2	0.8%
7.5	4	1.6%
7.6	4	1.6%
7.7	2	0.8%
7.8	4	1.6%
7.9	1	0.4%

Value	Count	Frequency (%)
220.4	1	0.4%
210.4	1	0.4%
200.2	1	0.4%
190.6	1	0.4%
181.3	1	0.4%
180.4	1	0.4%
177.3	1	0.4%
171.3	1	0.4%
168.2	1	0.4%
167.2	1	0.4%

ISI
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	106
Distinct (%)	43.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.759836066

Minimum	0
Maximum	19
Zeros	4
Zeros (%)	1.6%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0.3
Q1	1.4
median	3.5
Q3	7.3
95-th percentile	13.37
Maximum	19
Range	19
Interquartile range (IQR)	5.9

Descriptive statistics

Standard deviation	4.15462774
Coefficient of variation (CV)	0.8728510148
Kurtosis	0.8298604263
Mean	4.759836066
Median Absolute Deviation (MAD)	2.4
Skewness	1.126950083
Sum	1161.4
Variance	17.26093166
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
1.1	8	3.3%
1.2	7	2.9%
1	5	2.0%
4.7	5	2.0%
5.2	5	2.0%
2.8	5	2.0%
5.6	5	2.0%
0.4	5	2.0%
1.5	5	2.0%
0	4	1.6%
Other values (96)	190	77.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	4	1.6%
0.1	4	1.6%
0.2	4	1.6%
0.3	3	1.2%
0.4	5	2.0%
0.5	2	0.8%
0.6	4	1.6%
0.7	4	1.6%
0.8	3	1.2%
0.9	2	0.8%

Value	Count	Frequency (%)
19	1	0.4%
18.5	1	0.4%
17.2	1	0.4%
16.6	1	0.4%
16	1	0.4%
15.7	2	0.8%
15.5	1	0.4%
14.3	1	0.4%
14.2	1	0.4%
13.8	2	0.8%

BUI
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	173
Distinct (%)	70.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	16.67336066

Minimum	1.1
Maximum	68
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	1.1
5-th percentile	2.43
Q1	6
median	12.45
Q3	22.525
95-th percentile	46.35
Maximum	68
Range	66.9
Interquartile range (IQR)	16.525

Descriptive statistics

Standard deviation	14.20164844
Coefficient of variation (CV)	0.8517568071
Kurtosis	1.979913218
Mean	16.67336066
Median Absolute Deviation (MAD)	7.35
Skewness	1.458466015
Sum	4068.3
Variance	201.6868183
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
3	5	2.0%
5.1	4	1.6%
14.1	3	1.2%
12.5	3	1.2%
7.7	3	1.2%
10.9	3	1.2%
11.5	3	1.2%
4.4	3	1.2%
2.9	3	1.2%
2.4	3	1.2%
Other values (163)	211	86.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1.1	1	0.4%
1.4	2	0.8%
1.6	2	0.8%
1.7	2	0.8%
1.8	2	0.8%
2.2	1	0.4%
2.4	3	1.2%
2.6	2	0.8%
2.7	2	0.8%
2.8	2	0.8%

Value	Count	Frequency (%)
68	1	0.4%
67.4	1	0.4%
64	1	0.4%
62.9	1	0.4%
59.5	1	0.4%
59.3	1	0.4%
57.1	1	0.4%
54.9	1	0.4%
54.7	1	0.4%
50.9	1	0.4%

FWI
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	126
Distinct (%)	51.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.049180328

Minimum	0
Maximum	31.1
Zeros	9
Zeros (%)	3.7%
Negative	0
Negative (%)	0.0%
Memory size	2.0 KiB

Quantile statistics

Minimum	0
5-th percentile	0.1
Q1	0.7
median	4.45
Q3	11.375
95-th percentile	21.495
Maximum	31.1
Range	31.1
Interquartile range (IQR)	10.675

Descriptive statistics

Standard deviation	7.428365676
Coefficient of variation (CV)	1.05379141
Kurtosis	0.6553162089
Mean	7.049180328
Median Absolute Deviation (MAD)	4.05
Skewness	1.143242624
Sum	1720
Variance	55.18061661
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
0.4	12	4.9%
0.8	10	4.1%
0.1	9	3.7%
0	9	3.7%
0.5	9	3.7%
0.3	8	3.3%
0.9	7	2.9%
0.2	6	2.5%
0.7	5	2.0%
0.6	4	1.6%
Other values (116)	165	67.6%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	9	3.7%
0.1	9	3.7%
0.2	6	2.5%
0.3	8	3.3%
0.4	12	4.9%
0.5	9	3.7%
0.6	4	1.6%
0.7	5	2.0%
0.8	10	4.1%
0.9	7	2.9%

Value	Count	Frequency (%)
31.1	1	0.4%
30.3	1	0.4%
30.2	1	0.4%
30	1	0.4%
26.9	1	0.4%
26.3	1	0.4%
26.1	1	0.4%
25.4	1	0.4%
24.5	1	0.4%
24	1	0.4%

Classes
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

fire	138
not fire	106

Length

Max length	8
Median length	4
Mean length	5.737704918
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	not fire
2nd row	not fire
3rd row	not fire
4th row	not fire
5th row	not fire

Common Values

Value	Count	Frequency (%)
fire	138	56.6%
not fire	106	43.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
fire	244	69.7%
not	106	30.3%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Region
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct	2
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

2	122
1	122

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1
2nd row	1
3rd row	1
4th row	1
5th row	1

Common Values

Value	Count	Frequency (%)
2	122	50.0%
1	122	50.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2	122	50.0%
1	122	50.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Months
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

July	62
August	62
June	60
September	60

Length

Max length	9
Median length	5
Mean length	5.737704918
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	June
2nd row	June
3rd row	June
4th row	June
5th row	June

Common Values

Value	Count	Frequency (%)
July	62	25.4%
August	62	25.4%
June	60	24.6%
September	60	24.6%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
july	62	25.4%
august	62	25.4%
june	60	24.6%
september	60	24.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Class
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	0.8%
Missing	0
Missing (%)	0.0%
Memory size	2.0 KiB

1	138
0	106

Length

Max length	1
Median length	1
Mean length	1
Min length	1

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	0
2nd row	0
3rd row	0
4th row	0
5th row	0

Common Values

Value	Count	Frequency (%)
1	138	56.6%
0	106	43.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
1	138	56.6%
0	106	43.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	df_index	day	month	year	Temperature	RH	Ws	Rain	FFMC	DMC	DC	ISI	BUI	FWI	Classes	Region	Months	Class
0	0	1	6	2012	29	57	18	0.0	65.7	3.4	7.6	1.3	3.4	0.5	not fire	1	June	0
1	1	2	6	2012	29	61	13	1.3	64.4	4.1	7.6	1.0	3.9	0.4	not fire	1	June	0
2	2	3	6	2012	26	82	22	13.1	47.1	2.5	7.1	0.3	2.7	0.1	not fire	1	June	0
3	3	4	6	2012	25	89	13	2.5	28.6	1.3	6.9	0.0	1.7	0.0	not fire	1	June	0
4	4	5	6	2012	27	77	16	0.0	64.8	3.0	14.2	1.2	3.9	0.5	not fire	1	June	0
5	5	6	6	2012	31	67	14	0.0	82.6	5.8	22.2	3.1	7.0	2.5	fire	1	June	1
6	6	7	6	2012	33	54	13	0.0	88.2	9.9	30.5	6.4	10.9	7.2	fire	1	June	1
7	7	8	6	2012	30	73	15	0.0	86.6	12.1	38.3	5.6	13.5	7.1	fire	1	June	1
8	8	9	6	2012	25	88	13	0.2	52.9	7.9	38.8	0.4	10.5	0.3	not fire	1	June	0
9	9	10	6	2012	28	79	12	0.0	73.2	9.5	46.3	1.3	12.6	0.9	not fire	1	June	0

Last rows

	df_index	day	month	year	Temperature	RH	Ws	Rain	FFMC	DMC	DC	ISI	BUI	FWI	Classes	Region	Months	Class
234	236	21	9	2012	35	34	17	0.0	92.2	23.6	97.3	13.8	29.4	21.6	fire	2	September	1
235	237	22	9	2012	33	64	13	0.0	88.9	26.1	106.3	7.1	32.4	13.7	fire	2	September	1
236	238	23	9	2012	35	56	14	0.0	89.0	29.4	115.6	7.5	36.0	15.2	fire	2	September	1
237	239	24	9	2012	26	49	6	2.0	61.3	11.9	28.1	0.6	11.9	0.4	not fire	2	September	0
238	240	25	9	2012	28	70	15	0.0	79.9	13.8	36.1	2.4	14.1	3.0	not fire	2	September	0
239	241	26	9	2012	30	65	14	0.0	85.4	16.0	44.5	4.5	16.9	6.5	fire	2	September	1
240	242	27	9	2012	28	87	15	4.4	41.1	6.5	8.0	0.1	6.2	0.0	not fire	2	September	0
241	243	28	9	2012	27	87	29	0.5	45.9	3.5	7.9	0.4	3.4	0.2	not fire	2	September	0
242	244	29	9	2012	24	54	18	0.1	79.7	4.3	15.2	1.7	5.1	0.7	not fire	2	September	0
243	245	30	9	2012	24	64	15	0.2	67.3	3.8	16.5	1.2	4.8	0.5	not fire	2	September	0

Overview

Variables

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows